University of Cambridge > Talks.cam > CCIMI Seminars > Deep Denoising for Scientific Discovery

Deep Denoising for Scientific Discovery

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Hamza Fawzi.

Deep-learning approaches to denoising achieve impressive results when trained on standard image-processing datasets in a supervised fashion. However, unleashing their potential in practice will require developing unsupervised or semi-supervised approaches capable of learning from real data, as well as understanding the strategies learned by these models to perform denoising. In this talk, we will describe recent advances in this direction motivated by a real-world application to electron microscopy.

Join Zoom Meeting https://maths-cam-ac-uk.zoom.us/j/97537214061?pwd=MmthTUpDK1VVQ2RoWG8wU3BDdjVMQT09

Meeting ID: 975 3721 4061 Passcode: 010263

This talk is part of the CCIMI Seminars series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2021 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity